Display panels or monitors continue to increase in resolution, such as 1080 p, 4 k×2 k, etc. Many image sources have smaller resolutions than the displays. When the panel displays these sources in a display with higher resolution, traditional scaling techniques do not produce high fidelity results with rich details and sharp edges, etc. Examples of these traditional techniques include one-dimensional poly phase scaling (1D scaler), bilinear interpolation, bi-cubic interpolation, edge-guided scaling, etc. Super resolution techniques offer a solution to bridge the gap between advanced, higher resolution displays and low resolution video sources.
Multi-image super resolution processes construct a high resolution image from multiple low resolution images by fusing information among them. These algorithms can bring some details in smaller ratios of magnification in zooming. When larger ratios of magnification are needed, they cannot give enough details. Further, even at smaller ratios, if the motion is not in the right range, the ability of these techniques to reconstruct high frequency details suffers. These techniques also require many hardware resources and complex logic.
Different types of single frame super resolution (SFSR) techniques exist, including those that utilize gradient profiles or edge statistics. However, they generally do not recovery enough details. Some techniques apply many examples from external image training libraries. These methods typically have high hardware requirements to store trained examples, and often bring artifacts. Other techniques find the similarity information both in same scales and across different scales of the input image and usually use a subset of the more general fractal affine mapping technique. These are not easy to implement in hardware because of the complexity.
Some self-similarity super resolution techniques utilize similarity across different scales while ignoring similarity in same scales. Other techniques rely on self similarity being more common at the same scale but in different locations and as the scale changes the amount of self similarity decreases. Self-similarity super resolution research has also identified that that image zooming can be accomplished by modifying the Non-Local (NL) means denoising algorithm. The research also determined that super resolution using an external database of examples can be implemented by repeating the scaling several times, which reduces the size of the required database and allows for more flexibility in the final scaling ratio. For the most part, self-similarity super resolution process treat the entire image as the example data base and sometimes even multiple resolutions of the same image. This leads to a very expensive, if not physically impossible, hardware design.
Currently, no solutions exist that involve an inexpensive hardware solution that maintains all of the advantages of the full image approaches and have complexity based upon the output resolution required and not the input resolution required.